Add optional bihistogram parameter to plt.hist() #27180
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I think this is a better candidate for an example than an API expansion, setting the import numpy as np
import matplotlib.pyplot as plt
data1 = np.random.poisson(10, 2000)
data2 = np.random.poisson(20, 1000)
fig, ax = plt.subplots()
ax.hist(data1, bins=np.arange(40), label="Before")
ax.hist(data2, weights=-np.ones_like(data2), bins=np.arange(40), label="After")
ax.axhline(0, color="k")
ax.legend()
fig.savefig("test.png")Yields: |
Ah, that's beautiful and far simpler. Heh, kicking myself for not seeing that earlier. Thanks! |
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@i-jey would you be interested in adding Kyle's solution to maybe https://matplotlib.org/devdocs/gallery/statistics/histogram_features.html#sphx-glr-gallery-statistics-histogram-features-py as an example of using weights? |
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@story645 for sure! I’ll get to that today evening :) |
PR summary
This PR adds an optional boolean parameter to
plt.hist(),bihistto enable plotting bihistograms.Bihistograms are one way to visualize an effect on a distribution in a pre-post analysis, making it easier to see changes in the distribution's mean and skew.
PR checklist